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\author{\footnote{\url{jaron.kent-dobias@roma1.infn.it}}}
\affil{Istituto Nazionale di Fisica Nucleare, Sezione di Roma I, Italy}

\begin{document}

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\begin{center}{\Large \textbf{\color{scipostdeepblue}{
  On the topology of solutions to random continuous constraint satisfaction problems\\
}}}\end{center}

\begin{center}
  \textbf{Jaron Kent-Dobias$^\star$}
\end{center}

\begin{center}
Istituto Nazionale di Fisica Nucleare, Sezione di Roma I, Italy
\\[\baselineskip]
$\star$ \href{mailto:jaron.kent-dobias@roma1.infn.it}{\small jaron.kent-dobias@roma1.infn.it}
\end{center}

\section*{\color{scipostdeepblue}{Abstract}}
\textbf{\boldmath{%
We consider the set of solutions to $M$ random polynomial equations whose $N$
variables are restricted to the $(N-1)$-sphere. Each equation has independent
Gaussian coefficients and a target value $V_0$. When solutions exist, they form
a manifold. We compute the average Euler characteristic of this manifold in the
limit of large $N$, and find different behavior depending on the target value
$V_0$, the ratio $\alpha=M/N$, and the variances of the coefficients. We divide
this behavior into five phases with different implications for the topology of
the solution manifold. When $M=1$ there is a correspondence between this
problem and level sets of the energy in the spherical spin glasses. We
conjecture that the transition energy dividing two of the topological phases
corresponds to the asymptotic limit of gradient descent from a random initial
condition, possibly resolving a recent open problem in out-of-equilibrium
dynamics.
}
}

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\section{Introduction}

Constraint satisfaction problems seek configurations that simultaneously
satisfy a set of equations, and form a basis for thinking about problems as
diverse as neural networks \cite{Mezard_2009_Constraint}, granular materials
\cite{Franz_2017_Universality}, ecosystems \cite{Altieri_2019_Constraint}, and
confluent tissues \cite{Urbani_2023_A}. All but the last of these examples deal
with sets of inequalities, while the last considers a set of equality
constraints. Inequality constraints are familiar in situations like zero-cost
solutions in neural networks with ReLu activations and stable equilibrium in the
forces between physical objects. Equality constraints naturally appear in the
zero-gradient solutions to overparameterized smooth neural networks and in vertex models of tissues.

In such problems, there is great interest in characterizing structure in the
set of solutions, which can be influential in how algorithms behave when trying
to solve them \cite{Baldassi_2016_Unreasonable, Baldassi_2019_Properties,
Beneventano_2023_On}. Here, we show how topological information about
the set of solutions can be calculated in a simple model of satisfying random
nonlinear equalities. This allows us to reason about the connectivity of this
solution set. The topological properties revealed by this calculation yield
surprising results for the well-studied spherical spin glasses, where a
topological transition thought to occur at a threshold energy $E_\text{th}$
where marginal minima are dominant is shown to occur at a different energy
$E_\text{sh}$. We conjecture that this difference resolves an outstanding
problem in gradient descent dynamics in these systems.

We consider the problem of finding configurations $\mathbf x\in\mathbb R^N$
lying on the $(N-1)$-sphere $\|\mathbf x\|^2=N$ that simultaneously satisfy $M$
nonlinear constraints $V_k(\mathbf x)=V_0$ for $1\leq k\leq M$ and some
constant $V_0\in\mathbb R$. The nonlinear constraints are taken to be centered
Gaussian random functions with covariance
\begin{equation} \label{eq:covariance}
  \overline{V_i(\mathbf x)V_j(\mathbf x')}
  =\delta_{ij}f\left(\frac{\mathbf x\cdot\mathbf x'}N\right)
\end{equation}
for some choice of function $f$. When the covariance function $f$ is polynomial, the
$V_k$ are also polynomial, with a term of degree $p$ in $f$ corresponding to
all possible terms of degree $p$ in $V_k$. In particular, one can explicitly construct functions that satisfy \eqref{eq:covariance} by taking
\begin{equation}
  V_k(\mathbf x)
  =\sum_{p=0}^\infty\frac1{p!}\sqrt{\frac{f^{(p)}(0)}{N^p}}
  \sum_{i_1\cdots i_p}^NJ^{(k,p)}_{i_1\cdots i_p}x_{i_1}\cdots x_{i_p}
\end{equation}
with the elements of the tensors $J^{(k,p)}$ as independently distributed
unit normal random variables. The size of the
series coefficients of $f$ therefore control the variances in the coefficients
of the random polynomial constraints. When $M=1$, this problem corresponds to the
level set of a spherical spin glass with energy density $E=V_0/\sqrt{N}$.


This problem or small variations thereof have attracted attention recently for
their resemblance to encryption, least-squares optimization, and vertex models of confluent
tissues \cite{Fyodorov_2019_A, Fyodorov_2020_Counting,
  Fyodorov_2022_Optimization, Tublin_2022_A, Vivo_2024_Random, Urbani_2023_A, Kamali_2023_Dynamical,
  Kamali_2023_Stochastic, Urbani_2024_Statistical, Montanari_2023_Solving,
Montanari_2024_On, Kent-Dobias_2024_Conditioning, Kent-Dobias_2024_Algorithm-independent}. In each of these cases, the authors studied properties of
the cost function
\begin{equation} \label{eq:cost}
  \mathscr C(\mathbf x)=\frac12\sum_{k=1}^M\big[V_k(\mathbf x)-V_0\big]^2
\end{equation}
which achieves zero only for configurations that satisfy all the constraints.
Here we dispense with the cost function and study the set of solutions
directly.
This set
can be written as
\begin{equation}
  \Omega=\big\{\mathbf x\in\mathbb R^N\mid \|\mathbf x\|^2=N,V_k(\mathbf x)=V_0
  \;\forall\;k=1,\ldots,M\big\}
\end{equation}
Because the constraints are all smooth functions, $\Omega$ is almost always a manifold without singular points.\footnote{The conditions for a singular point are that
  $0=\frac\partial{\partial\mathbf x}V_k(\mathbf x)$ for all $k$. This is
  equivalent to asking that the constraints $V_k$ all have a stationary point at
  the same place. When the $V_k$ are independent and random, this is vanishingly
  unlikely, requiring $NM+1$ independent equations to be simultaneously satisfied.
  This means that different connected components of the set of solutions do not
intersect, nor are there self-intersections, without extraordinary fine-tuning.}
We study the topology of the manifold $\Omega$ by computing its
average Euler characteristic, a topological invariant whose value puts
constraints on the structure of the manifold. The topological phases determined
by this means are distinguished by the size and sign of the Euler
characteristic, and the distribution in space of its constituent parts.

\section{The average Euler characteristic}

\subsection{Definition and derivation}

The Euler characteristic $\chi$ of a manifold is a topological invariant \cite{Hatcher_2002_Algebraic}. It is
perhaps most familiar in the context of connected compact orientable surfaces, where it
characterizes the number of handles in the surface: $\chi=2(1-\#)$ for $\#$
handles. For general $d$, the Euler characteristic of the $d$-sphere is $2$ if $d$ is even and 0 if $d$ is odd. The canonical method for computing the Euler characteristic is done by
defining a complex on the manifold in question, essentially a
higher-dimensional generalization of a polygonal tiling. Then $\chi$ is given
by an alternating sum over the number of cells of increasing dimension, which
for 2-manifolds corresponds to the number of vertices, minus the number of
edges, plus the number of faces.

Morse theory offers another way to compute the Euler characteristic of a manifold $\Omega$ using the
statistics of stationary points in a function $H:\Omega\to\mathbb R$ \cite{Audin_2014_Morse}. For
functions $H$ without any symmetries with respect to the manifold, the surfaces
of gradient flow between adjacent stationary points form a complex. The
alternating sum over cells to compute $\chi$ becomes an alternating sum over
the count of stationary points of $H$ with increasing index, or
\begin{equation}
  \chi(\Omega)=\sum_{i=0}^N(-1)^i\mathcal N_H(\text{index}=i)
\end{equation}
Conveniently, we can express this sum as an integral over the manifold
using a small variation on the Kac--Rice formula for counting stationary
points \cite{Kac_1943_On, Rice_1939_The}. Since the sign of the determinant of the Hessian matrix of $H$ at a
stationary point is equal to its index, if we count stationary points including
the sign of the determinant, we arrive at the Euler characteristic, or
\begin{equation} \label{eq:kac-rice}
  \chi(\Omega)=\int_\Omega d\mathbf x\,\delta\big(\nabla H(\mathbf x)\big)\det\operatorname{Hess}H(\mathbf x)
\end{equation}
When the Kac--Rice formula is used to \emph{count} stationary points, one must take pains to preserve the sign of the determinant
\cite{Fyodorov_2004_Complexity}. Here we are correct to exclude it.

We need to choose a function $H$ for our calculation. Because $\chi$ is
a topological invariant, any choice will work so long as it does not share some
symmetry with the underlying manifold, i.e., that $H$ satisfies the Smale condition. Because our manifold of random
constraints has no symmetries, we can take a simple height function $H(\mathbf
x)=\mathbf x_0\cdot\mathbf x$ for some $\mathbf x_0\in\mathbb R^N$ with
$\|\mathbf x_0\|^2=N$. $H$ is a height function because when $\mathbf x_0$ is
used as the polar axis, $H$ gives the height on the sphere relative to the equator.

We treat the integral over the implicitly defined manifold $\Omega$ using the
method of Lagrange multipliers. We introduce one multiplier $\omega_0$ to
enforce the spherical constraint and $M$ multipliers $\omega_k$ to enforce the vanishing of
each of the $V_k$, resulting in the Lagrangian
\begin{equation} \label{eq:lagrangian}
  L(\mathbf x,\pmb\omega)
  =H(\mathbf x)+\frac12\omega_0\big(\|\mathbf x\|^2-N\big)
  +\sum_{k=1}^M\omega_k\big(V_k(\mathbf x)-V_0\big)
\end{equation}
The integral over $\Omega$ in \eqref{eq:kac-rice} then becomes
\begin{equation} \label{eq:kac-rice.lagrange}
  \chi(\Omega)=\int_{\mathbb R^N} d\mathbf x\int_{\mathbb R^{M+1}}d\pmb\omega
  \,\delta\big(\partial L(\mathbf x,\pmb\omega)\big)
  \det\partial\partial L(\mathbf x,\pmb\omega)
\end{equation}
where $\partial=[\frac\partial{\partial\mathbf x},\frac\partial{\partial\pmb\omega}]$
is the vector of partial derivatives with respect to all $N+M+1$ variables.
This integral is now in a form where standard techniques from mean-field theory
can be applied to calculate it.

To evaluate the average of $\chi$ over the constraints, we first translate the $\delta$-functions and determinant to integral form, with
\begin{align}
  \label{eq:delta.exp}
  \delta\big(\partial L(\mathbf x,\pmb\omega)\big)
  &=\int\frac{d\hat{\mathbf x}}{(2\pi)^N}\frac{d\hat{\pmb\omega}}{(2\pi)^{M+1}}
  e^{i[\hat{\mathbf x},\hat{\pmb\omega}]\cdot\partial L(\mathbf x,\pmb\omega)}
  \\
  \label{eq:det.exp}
  \det\partial\partial L(\mathbf x,\pmb\omega)
  &=\int d\bar{\pmb\eta}\,d\pmb\eta\,d\bar{\pmb\gamma}\,d\pmb\gamma\,
  e^{-[\bar{\pmb\eta},\bar{\pmb\gamma}]^T\partial\partial L(\mathbf x,\pmb\omega)[\pmb\eta,\pmb\gamma]}
\end{align}
where $\hat{\mathbf x}$ and $\hat{\pmb\omega}$ are ordinary vectors and
$\bar{\pmb\eta}$, $\pmb\eta$, $\bar{\pmb\gamma}$, and $\pmb\gamma$ are
Grassmann vectors. With these expressions substituted into
\eqref{eq:kac-rice.lagrange}, the result is an integral over an exponential
whose argument is linear in the random functions $V_k$. These functions can
therefore be averaged over, and the resulting expression treated with standard
methods. Details of this calculation can be found in Appendix~\ref{sec:euler}.
The result is the reduction of the average Euler characteristic to an expression of the
form
\begin{equation} \label{eq:pre-saddle.characteristic}
  \overline{\chi(\Omega)}
  =\left(\frac N{2\pi}\right)^2\int dR\,dD\,dm\,d\hat m\,g(R,D,m,\hat m)\,e^{N\mathcal S_\chi(R,D,m,\hat m)}
\end{equation}
where $g$ is a prefactor of $o(N^0)$, and $\mathcal S_\chi$ is an effective action defined by
\begin{equation} \label{eq:euler.action}
  \begin{aligned}
    \mathcal S_\chi(R,D,m,\hat m\mid\alpha,V_0)
    &=-\hat m-\frac\alpha2\left[
      \log\left(1+\frac{f(1)D}{f'(1)R^2}\right)
      +\frac{V_0^2}{f(1)}\left(1+\frac{f'(1)R^2}{f(1)D}\right)^{-1}
    \right] \\
    &\hspace{7em}+\frac12\log\left(
      1+\frac{(1-m^2)D+\hat m^2-2Rm\hat m}{R^2}
    \right)
  \end{aligned}
\end{equation}
The remaining order parameters are defined by the scalar products
\begin{align}
  R=-i\frac1N\mathbf x\cdot\hat{\mathbf x}
  &&
  D=\frac1N\hat{\mathbf x}\cdot\hat{\mathbf x}
  &&
  m=\frac1N\mathbf x\cdot\mathbf x_0
  &&
  \hat m=-i\frac1N\hat {\mathbf x}\cdot\mathbf x_0
\end{align}

\subsection{Features of the effective action}

This integral can be evaluated to leading order by a saddle point method. For reasons we will
see, it is best to extremize with respect to $R$, $D$, and $\hat m$, leaving a
new effective action of $m$ alone. This can be solved to give
\begin{equation}
  D=-\frac{m+R_*}{1-m^2}R_* \qquad \hat m=0
\end{equation}
\begin{equation} \label{eq:rs}
  \begin{aligned}
    R_*
    =\frac{-m(1-m^2)}{2[f(1)-(1-m^2)f'(1)]^2}
    \Bigg[
      \alpha V_0^2f'(1)
      +(2-\alpha)f(1)\left(\frac{f(1)}{1-m^2}-f'(1)\right) \quad \\
    \quad+\alpha\sqrt{
      \tfrac{4V_0^2}\alpha f(1)f'(1)\left[\tfrac{f(1)}{1-m^2}-f'(1)\right]
      +\left[\tfrac{f(1)^2}{1-m^2}-\big(V_0^2+f(1)\big)f'(1)\right]^2
    }
  \Bigg]
  \end{aligned}
\end{equation}
with the resulting effective action as a function of $m$ alone given by
\begin{equation} \label{eq:S.m}
  \mathcal S_\chi(m)
  =-\frac\alpha2\bigg[
    \log\left(
      1-\frac{f(1)}{f'(1)}\frac{1+\frac m{R_*}}{1-m^2}
    \right)
    +\frac{V_0^2}{f(1)}\left(
      1-\frac{f'(1)}{f(1)}\frac{1-m^2}{1+\frac m{R_*}}
    \right)^{-1}
  \bigg]
  +\frac12\log\left(-\frac m{R_*}\right)
\end{equation}
This function is plotted as a function of $m$ in Fig.~\ref{fig:action} for a variety of $V_0$ and $f$.
To finish evaluating the integral, this expression should be maximized with
respect to $m$. If $m_*$ is such a maximum, then $\overline{\chi(\Omega)}\propto e^{N\mathcal S_\chi(m_*)}$. The order parameter $m$ is the overlap of the configuration $\mathbf x$ with the height axis
$\mathbf x_0$. Therefore, the value $m$ that maximizes this action can be
understood as the latitude on the sphere where most of the contribution to the
Euler characteristic is made.

\begin{figure}
  \includegraphics{figs/action_1.pdf}
  \hspace{-3.5em}
  \includegraphics{figs/action_3.pdf}

  \caption{
    \textbf{Effective action for the Euler characteristic.}
    The effective action \eqref{eq:S.m} governing the average Euler characteristic as a function of the overlap
    $m=\frac1N\mathbf x\cdot\mathbf x_0$ with the height axis for two
    different homogeneous polynomial constraints and a variety of target values $V_0$. Dashed lines depict $\operatorname{Re}\mathcal S_\chi$ when its imaginary part is nonzero. In both
    plots $\alpha=\frac12$. \textbf{Left:} With linear functions there are two
    regimes. For small $V_0$, there are maxima at $m=\pm m^*$ where the action
    is zero, while after the satisfiability transition at $V_0=V_{\text{\textsc{sat}}\ast}=1$, $m_*$
    goes to zero and the action becomes negative everywhere. \textbf{Right:} With nonlinear
    functions there are other possible regimes. For small $V_0$, there are maxima at $m=\pm m^*$ where the action is zero but the real part of the action is maximized at $m=0$ where the action is complex. For larger $V_0\geq V_\text{on}\simeq1.099$ the maxima at $m=\pm m^*$ disappear. For $V_0\geq V_\text{sh}\simeq1.394$ larger still, the action becomes real everywhere.
    Finally, at a satisfiability transition
    $V_0=V_\text{\textsc{sat}}\simeq1.440$ the action becomes negative everywhere.
  } \label{fig:action}
\end{figure}

The action $\mathcal S_\chi$ is extremized with respect to $m$ at $m=0$ or $m=\pm m_*=\mp R_*(m_*)$ for
\begin{equation}
  m^*=\sqrt{1-\frac{\alpha}{f'(1)}\big(V_0^2+f(1)\big)}
\end{equation}
and $\mathcal S_\chi(m^*)=0$. Zero action implies that
$\overline{\chi(\Omega)}$ does not vary exponentially with $N$, and in fact we
show in Appendix~\ref{sec:prefactor} that the contribution from each of these
maxima is $(-1)^{N-M-1}+o(N^0)$, so that their sum is $2$ in even dimensions and $0$ in odd dimensions. This result is consistent with the topology of an $N-M-1$ sphere.

If this solution were always well-defined, it would
vanish when the argument of the square root vanishes at
\begin{equation}
  V_0^2>V_{\text{\textsc{sat}}\ast}^2\equiv\frac{f'(1)}\alpha-f(1)
\end{equation}
This corresponds precisely to the satisfiability transition found in previous work by a replica symmetric analysis of the cost function \eqref{eq:cost} \cite{Fyodorov_2019_A, Fyodorov_2020_Counting, Fyodorov_2022_Optimization, Tublin_2022_A, Vivo_2024_Random}.
However, the action becomes complex in the region $m^2<m_\text{min}^2$ for
\begin{equation}
  m_\text{min}^2
  =1-\frac{f(1)^2}{f'(1)}\frac{V_0^2(1+\sqrt{1-\alpha})^2-\alpha f(1)}{
    4V_0^2f(1)-\alpha[V_0^2+f(1)]^2
  }
\end{equation}
When $m_*^2<m_\text{min}^2$, the solution at $m=\pm m_*$ no longer maximizes
the action. This happens when the target value is larger than an onset value $V_\text{on}$ defined by
\begin{equation}
  V_0^2>V_\text{on}^2\equiv\frac{f(1)}\alpha\left(1-\alpha+\sqrt{1-\alpha}\right)
\end{equation}
Comparing this with the satisfiability transition associated with $m_*$ going to zero, one sees
\begin{equation}
  V_\text{on}^2-V_{\text{\textsc{sat}}\ast}^2
  =\frac1\alpha\left(f'(1)-f(1)-f(1)\sqrt{1-\alpha}\right)
\end{equation}
If $f(q)$ is purely linear, then $f'(1)=f(1)$ and
$V_\text{on}^2>V_{\text{\textsc{sat}}\ast}^2$, so the naïve satisfiability
transition happens first. On the other hand, when $f(q)$ contains powers of $q$
strictly greater than 1, then $f'(1)\geq 2f(1)$ and $V_\text{on}^2\leq
V_{\text{\textsc{sat}}\ast}^2$, so the onset happens first. In situations with
mixed constant, linear, and nonlinear terms in $f$, the order of the
transitions depends on the precise form of $f$.

Likewise, the solution at $m=0$ is sometimes complex-valued and sometimes real-valued. For $V_0$ less than a shattering value $V_\text{sh}$ defined by
\begin{equation}
  V_0^2<V_\text{sh}^2\equiv\frac{f(1)}\alpha\left(1-\frac{f(1)}{f'(1)}\right)\left(1+\sqrt{1-\alpha}\right)^2
\end{equation}
the maximum at $m=0$ is complex while for $V_0$ greater than this value the action is real. For purely
linear $f(q)$, $V_\text{sh}=0$ and the action at $m=0$ is always real, though
for $V_0^2<V_{\text{\textsc{sat}}\ast}^2$ it is a minimum rather than a
maximum. Finally, there is another satisfiability transition at
$V=V_\text{\textsc{sat}}$ corresponding to the vanishing of the effective
action at the $m=0$ solution, with $\mathcal S(0)=0$. For generic covariance
function $f$ it is not possible to write an explicit formula for
$V_\text{\textsc{sat}}$, and we usually calculate it through a numeric
root-finding algorithm.

When $m_\text{min}^2>0$, the solution at $m=0$ is difficult to interpret, since the action takes a complex value. In fact, it is not
clear what the contribution to the average value of the Euler characteristic should be at all when
there is some range $-m_\text{min}<m<m_\text{min}$ where the effective action
is complex. Such a result could arise from the breakdown of the large-deviation
principle behind the calculation of the effective action, or it could be the
result of a negative Euler characteristic.

To address this ambiguity, we compute also the average of the square of the Euler
characteristic, $\overline{\chi(\Omega)^2}$, with details in
Appendix~\ref{sec:rms}. This has the benefit of always being positive, so that
the saddle-point approach to the calculation at large $N$ does not produce
complex values even when $\overline{\chi(\Omega)}$ is negative. Under the restriction that $f(0)=0$,\footnote{
  This restriction is equivalent to having no random constant term in the
  constraint equations. It provides a simplification here because when it is
  present the replica symmetric (\textsc{rs}) description of this problem can
  have $q_0>0$, and $\overline{\chi(\Omega)^2}\neq[\overline{\chi(\Omega)}]^2$
  always.
}we find three
saddle points that could contribute to the value of
$\overline{\chi(\Omega)^2}$: two at $\pm m^*$ where
$\frac1N\log\overline{\chi(\Omega)^2}=\frac1N\log\overline{\chi(\Omega)}\simeq0$,
and one at $m=0$ where
\begin{equation}
  \frac1N\log\overline{\chi(\Omega)^2}=2\operatorname{Re}\mathcal S_\chi(0)
\end{equation}
which is consistent with
$\overline{\chi(\Omega)^2}\simeq[\overline{\chi(\Omega)}]^2$. It is therefore possible to interpret the average Euler characteristic in the
regime where its effective action is complex-valued as being negative, with a
magnitude implied by the real part of the action.

Such a
correspondence, which indicates that the `annealed' calculation here is also
representative of typical realizations of the constraints, is not always true.
With average squared Euler characteristic we find instabilities of the solution
at $m=0$ to replica symmetry breaking (\textsc{rsb}). We do not explore
these \textsc{rsb} solutions here, except in the context of $M=1$ in
Section~\ref{sec:ssg}. However, in the following Figures \ref{fig:phases} and
\ref{fig:crossover} we shade the unstable region.

\subsection{Topological phases and their interpretation}

The results of the previous section allow us to unambiguously define distinct
topological phases, which differ depending on the presence or absence of the
local maxima at $m=\pm m^*$, on the presence or absence of the local maximum
at $m=0$, on the real or complex nature of this maximum, and finally on
whether the action is positive or negative. Below we enumerate these regimes,
which are schematically represented in Fig.~\ref{fig:cartoons}.\footnote{
  In the following we characterize regimes by values of
  $\overline{\chi(\Omega)}$. These should be understood as their values in
  \emph{even} dimensions, since in odd dimensions the Euler characteristic is
  always identically zero. We do not expect the qualitative results to change
  depending on the evenness or oddness of the manifold dimension.
}

\paragraph{Regime I: \boldmath{$\overline{\chi(\Omega)}=2$}.}

This regime is found when the magnitude of the target value $V_0$ is less than
the onset $V_\text{on}$ and $\operatorname{Re}\mathcal S(0)<0$, so that the
maxima at $m=\pm m^*$ exist and are the dominant contributions to the average
Euler characteristic. Here, $\overline{\chi(\Omega)}=2+o(1)$ for even $N-M-1$,
strongly indicating a topology homeomorphic to the $S^{N-M-1}$ sphere. This regime is the only nontrivial one found with linear covariance $f(q)$, where the solution manifold must be a sphere if it is not empty.

\paragraph{Regime II: \boldmath{$\overline{\chi(\Omega)}$} large and negative, isolated contributions at \boldmath{$m=\pm m^*$}.}

This regime is found when the magnitude of the target value $V_0$ is less than the onset $V_\text{on}$, $\operatorname{Re}\mathcal S(0)>0$, and the value of the action at $m=0$ is complex. The dominant contribution to the average Euler characteristic comes from the equator at $m=0$, but the complexity of the action implies that the Euler characteristic is negative.
While the topology of the manifold is not necessarily connected in this
regime, holes are more numerous than components. Since $V_0^2<V_\text{on}^2$,
there are isolated contributions to $\overline{\chi(\Omega)}$ at $m=\pm m^*$.
This implies a temperate band of relative simplicity: given a random point on
the sphere, the nearest parts of the solution manifold are unlikely to have holes or
disconnected components.

\paragraph{Regime III: \boldmath{$\overline{\chi(\Omega)}$} large and negative, no contribution at \boldmath{$m=\pm m^*$}.}

The same as Regime II, but with $V_0^2>V_\text{on}^2$. The solutions at
$m=\pm m_*$ no longer exist, and nontrivial contributions to the Euler
characteristic are made all the way to the solution manifold's boundary.

\paragraph{Regime IV: \boldmath{$\overline{\chi(\Omega)}$} large and positive.}

This regime is found when the magnitude of the target value $V_0$ is greater
than the shattering value $V_\text{sh}$ and $\mathcal S(0)>0$. Above the
shattering transition the effective action is real everywhere, and its value at
the equator is the dominant contribution. Large connected components of the
manifold may or may not exist, but small disconnected components outnumber
holes.

\paragraph{Regime V: \boldmath{$\overline{\chi(\Omega)}$} very small.}

Here $\frac1N\log\overline{\chi(\Omega)}<0$, indicating that the average
Euler characteristic shrinks exponentially with $N$. Under most conditions
we conclude this is the \textsc{unsat} regime where no manifold exists, but
there may be circumstances where part of this regime is characterized by
nonempty solution manifolds that are overwhelmingly likely to have Euler
characteristic zero.

\begin{figure}
  \includegraphics[width=0.196\textwidth]{figs/connected.pdf}
  \includegraphics[width=0.196\textwidth]{figs/middle.pdf}
  \includegraphics[width=0.196\textwidth]{figs/complex.pdf}
  \includegraphics[width=0.196\textwidth]{figs/shattered.pdf}
  \includegraphics[width=0.196\textwidth]{figs/gone.pdf}

  \hspace{1.5em}
  \textbf{Regime I}
  \hfill
  \textbf{Regime II}
  \hfill
  \textbf{Regime III}
  \hfill
  \textbf{Regime IV}
  \hfill
  \textbf{Regime V}
  \hspace{1.5em}

  \caption{
    \textbf{Cartoons of the solution manifold in five topological regimes.}
    The solution manifold is shown as a shaded region with a black boundary,
    and the height axis $\mathbf x_0$ is a black arrow. In Regime I, the
    statistics of the Euler characteristic is consistent with a manifold with a
    single simply-connected component. In Regime II, holes occupy the equator
    but its most polar regions are
    topologically simple. In Regime III, holes dominate and the edge of the
    manifold is not necessarily simple. In Regime IV, disconnected components
    dominate. In Regime V, the manifold is empty.
  } \label{fig:cartoons}
\end{figure}


\begin{figure}
  \includegraphics{figs/phases_1.pdf}
  \hspace{-3em}
  \includegraphics{figs/phases_2.pdf}
  \hspace{-3em}
  \includegraphics{figs/phases_3.pdf}

  \caption{
    \textbf{Topological phase diagram.}
    Topological phases of the model for three different homogeneous covariance
    functions. The regimes are defined in the text and depicted as cartoons in
    Fig.~\ref{fig:cartoons}. The shaded region in the center panel shows where
    these results are unstable to \textsc{rsb}. In the limit of $\alpha\to0$,
    the behavior of level sets of the spherical spin glasses are recovered: the
    righthand plot shows how the ground state energy
    $E_\text{gs}$ and threshold energy $E_\text{th}$ of the 3-spin spherical model correspond with the limits
    of the satisfiability and shattering transitions in the pure cubic model. Note that
    for mixed models with inhomogeneous covariance functions, $E_\text{th}$ is
    not the lower limit of $V_\text{sh}$.
  } \label{fig:phases}
\end{figure}

\paragraph{}

The distribution of these phases for situations with homogeneous polynomial
constraint functions is shown in Fig.~\ref{fig:phases}. For purely linear
models, the only two regimes are I and V, separated by a satisfiability
transition at $V_{\text{\textsc{sat}}\ast}$. This is expected: the intersection
of a plane and a sphere is another sphere, and therefore a model of linear
constraints in a spherical configuration space can only produce a solution
manifold consisting of a single sphere, or the empty set. For purely nonlinear
models, regime I does not appear, while the other three nontrivial regimes do.
Regimes II and III are separated by the onset transition at $V_\text{on}$,
while III and IV are separated by the shattering transition at $V_\text{sh}$.
Finally, IV and V are now separated by the satisfiability transition at
$V_\text{\textsc{sat}}$.

One interesting
feature occurs in the limit of $\alpha$ to zero. If $V_0$ is likewise rescaled
in the correct way, the limit of these phase boundaries approaches known
landmark energy values in the pure spherical spin glasses. In particular, the
limit to zero $\alpha$ of the scaled satisfiability transition
$V_\text{\textsc{sat}}\sqrt\alpha$ approaches the ground state energy
$E_\text{gs}$, while the limit to zero $\alpha$ of the scaled shattering
transition $V_\text{sh}\sqrt\alpha$ approaches the threshold energy
$E_\text{th}$. The correspondence between ground state and satisfiability is
expected: when the energy of a level set is greater in magnitude than the
ground state, the level set will usually be empty. The correspondence between
the threshold and shattering energies is also intuitive, since the threshold
energy is typically understood as the point where the landscape fractures into
pieces. However, this second correspondence is only true for the pure spherical
models with homogeneous $f(q)$. For any other model with an inhomogeneous
$f(q)$, $E_\text{sh}^2<E_\text{th}^2$. This may have implications for dynamics
in such mixed models, and we discuss it at length in Section~\ref{sec:ssg}.

\begin{figure}
  \includegraphics{figs/phases_12_1.pdf}
  \hspace{-2.85em}
  \includegraphics{figs/phases_12_2.pdf}
  \hspace{-2.85em}
  \includegraphics{figs/phases_12_3.pdf}
  \hspace{-2.85em}
  \includegraphics{figs/phases_12_4.pdf}

  \caption{
    \textbf{Linear--quadratic crossover.}
    Topological phases for models with a covariance function
    $f(q)=(1-\lambda)q+\lambda\frac12q^2$ for several values of $\lambda$,
    interpolating between homogeneous linear ($\lambda=0$) and quadratic
    ($\lambda=1$) constraints. The regimes are defined in the text and depicted
    as cartoons in Fig.~\ref{fig:cartoons}. The shaded region on each plot
    shows where these results are unstable to \textsc{rsb}.
  } \label{fig:crossover}
\end{figure}

Rich coexistence between all four regimes occurs in models with mixed linear
and nonlinear constraints. Fig.~\ref{fig:crossover} shows examples of the phase
diagrams for models with a covariance function that interpolates between pure
linear ($\lambda=0$) and pure quadratic ($\lambda=1$). A new phase boundary
appears separating regimes I and II, defined as the point where the real part
of the action at $m=0$ changes from negative to positive. In purely quadratic
case, and in mixed linear and nonlinear cases, there is a substantial region of
the phase diagram shown in Appendix~\ref{sec:rms} to be susceptible to
\textsc{rsb}, especially for small $V_0$ and large $\alpha$. Future research
into the structure of solutions in this regime is merited.

\section{Implications for the dynamics of spherical spin glasses}
\label{sec:ssg}

When $M=1$ the solution manifold corresponds to the energy
level set of a spherical spin glass with energy density $E=\sqrt NV_0$. All the
results from the previous sections follow, and can be translated to the spin
glasses by taking the limit $\alpha\to0$ while keeping $E=V_0\alpha^{-1/2}$ fixed. With a little algebra this procedure yields
\begin{align}
  E_\text{on}=\pm\sqrt{2f(1)}
  &&
  E_\text{sh}=\pm\sqrt{4f(1)\left(1-\frac{f(1)}{f'(1)}\right)}
  \label{eq:ssg.energies}
\end{align}
for the onset and shattering energies. For the pure $p$-spin spherical spin glasses, which have homogeneous covariances $f(q)=\frac12q^p$,
$E_\text{sh}=\sqrt{2(p-1)/p}$, precisely the threshold energy in these
models \cite{Castellani_2005_Spin-glass}. This is intuitive, since threshold energy, defined as the place where
marginal minima are dominant in the landscape, is widely understood as the
place where level sets are broken into pieces.

However, for general mixed models the threshold energy is
\begin{equation}
  E_\mathrm{th}=\pm\frac{f(1)[f''(1)-f'(1)]+f'(1)^2}{f'(1)\sqrt{f''(1)}}
\end{equation}
which satisfies $|E_\text{sh}|\leq|E_\text{th}|$. Therefore, as one
descends in energy in generic models one will meet the shattering energy before
the threshold energy. This is perhaps unexpected, since one might imagine that
where level sets of the energy break into many pieces would coincide with the
largest concentration of shallow minima in the landscape. We see here that this isn't the case.

This fact mirrors another another that was made clear
recently: when gradient decent dynamics are run on these models, they will
asymptotically reach an energy above the threshold energy
\cite{Folena_2020_Rethinking, Folena_2021_Gradient, Folena_2023_On}. The old
belief that the threshold energy qualitatively coincides with a kind of
shattering is the origin of the expectation that the dynamic limit should be
the threshold energy. Given that our work shows the actual shattering energy is
different, here we compare it with data on the asymptotic limits of dynamics to
see if they coincide.

\begin{figure}
  \includegraphics{figs/dynamics_2.pdf}
  \hspace{-0.5em}
  \includegraphics{figs/dynamics_3.pdf}

  \caption{
    \textbf{Is the shattering energy a dynamic threshold?} Comparison of the shattering energy $E_\text{sh}$ with the asymptotic
    performance of gradient descent from a random initial condition in $p+s$
    models with $p=2$ and $p=3$ and varying $s$. The values of $\lambda$ depend on $p$ and $s$ and are taken from \cite{Folena_2023_On}. The points show the asymptotic performance
    extrapolated using two different methods and have unknown uncertainty, from \cite{Folena_2023_On}. Also
    shown is the annealed threshold energy $E_\text{th}$, where marginal minima
    are the most common type of stationary point. The section of $E_\text{sh}$
    that is dashed on the left plot indicates the continuation of the annealed
    result, whereas the solid portion gives the value calculated with a
    {\oldstylenums 1}\textsc{frsb} ansatz.
  } \label{fig:ssg}
\end{figure}

Measurements of the asymptotic limits of dynamics were recently taken in
\cite{Folena_2023_On} for two different classes of models with inhomogeneous
$f(q)$, with
\begin{equation}
  f(q)=\frac12\big[\lambda q^p+(1-\lambda)q^s\big]
\end{equation}
They studied models with this covariance for $p=2$ and $p=3$ while varying $s$.
In both cases, the relative weight $\lambda$ varies with $s$ and was chosen to
maximize a heuristic to increase the chances of seeing nontrivial behavior. The
authors numerically integrated the dynamic mean field theory (\textsc{dmft})
equations for gradient descent on these models from a random initial condition to large but finite time, then attempted to
extrapolate the infinite-time behavior by two different methods. The black
symbols in Fig.~\ref{fig:ssg} show the measurements taken from
\cite{Folena_2023_On}. The difference between the two extrapolations is not
critical here, see the original paper for details. We simply note that the
authors of \cite{Folena_2023_On} did not associate an uncertainty with them,
nor were they confident that they are unbiased estimates of the asymptotic value.

Fig.~\ref{fig:ssg} also shows the shattering and {\oldstylenums1}\textsc{rsb}
threshold energies as a function of $s$. The solid lines come from using
\textsl{Mathematica}'s \texttt{Interpolation} function to create a smooth
function $\lambda(s)$ through the values used in \cite{Folena_2023_On}. For the
$2+s$ models for sufficiently large $s$, the ground state is described by a
{\oldstylenums1}\textsc{frsb} order and both the threshold energy and
shattering energy calculated using an annealed average are likely inaccurate
\cite{Auffinger_2022_The}. In Appendix~\ref{sec:1frsb} we calculate the
quenched ground state and shattering energies for these models consistent with
the {\oldstylenums1}\textsc{frsb} equilibrium order. In the left panel of Fig.~\ref{fig:ssg}, the solid line shows the quenched calculation, while the dashed line shows the annealed formula \eqref{eq:ssg.energies}.

Is the shattering energy consistent with the dynamic threshold for gradient
descent from a random initial condition? The evidence in Fig.~\ref{fig:ssg} is
compelling but inconclusive. The difference between the shattering energy and
the extrapolated \textsc{dmft} data is about the same as the difference between
the values predicted by the two extrapolation methods. If both extrapolation
methods suffer from similar systematic biases, it is plausible the true value is the shattering energy. However, better estimates of the
asymptotic values are needed to support or refute this conjecture. This
motivates working to integrate the \textsc{dmft} equations to longer times, or
else look for analytic asymptotic solutions that approach $E_\text{sh}$.

\section{Conclusion}

We have shown how to calculate the average Euler characteristic of the solution
manifold in a simple model of random constraint satisfaction. The results
constrain the topology and geometry of this manifold, revealing when it is
connected and trivial, when it is extensive but topologically nontrivial, and
when it is shattered into disconnected pieces.

This calculation has novel implications for the geometry of the energy
landscape in the spherical spin glasses, where it reveals a previously unknown
landmark energy $E_\text{sh}$. This shattering energy is where the topological
calculation implies that the level set of the energy breaks into disconnected
pieces, and differs from the threshold energy $E_\text{th}$ in mixed models.
It's possible that $E_\text{sh}$ is the asymptotic limit of gradient descent
from a random initial condition in such models, but the quality of the
currently available data makes this conjecture inconclusive.

This paper has focused on equality constraints, while most existing studies of
constraint satisfaction study inequality constraints \cite{Franz_2016_The,
Franz_2017_Universality, Franz_2019_Critical, Annesi_2023_Star-shaped,
Baldassi_2023_Typical}. To generalize the technique developed in this paper to
such cases is not a trivial extension. The set of solutions to such problems
are manifolds with boundary, and these boundaries are often not smooth. To
study such cases with these techniques will require using extensions of the
Morse theory for manifolds with boundary, and will be the subject of future work.

\paragraph{Acknowledgements}
The authors thank Pierfrancesco Urbani for helpful conversations on these
topics, and Giampaolo Folena for supplying his \textsc{dmft} data for the
spherical spin glasses.

\paragraph{Funding information}
JK-D is supported by a \textsc{DynSysMath} Specific Initiative of the INFN.

\appendix

\section{Details of the calculation of the average Euler characteristic}
\label{sec:euler}

Our starting point is the expression \eqref{eq:kac-rice.lagrange} with the
substitutions of the $\delta$-function and determinant \eqref{eq:delta.exp} and
\eqref{eq:det.exp} made. To make the calculation compact, we introduce
superspace coordinates \cite{DeWitt_1992_Supermanifolds}. Introducing the Grassmann indices $\bar\theta_1$
and $\theta_1$, we define the supervectors
\begin{align}
  \pmb\phi(1)=\mathbf x+\bar\theta_1\pmb\eta+\bar{\pmb\eta}\theta_1+\bar\theta_1\theta_1i\hat{\mathbf x}
  &&
  \sigma_k(1)=\omega_k+\bar\theta_1\gamma_k+\bar\gamma_k\theta_1+\bar\theta_1\theta_1i\hat\omega_k
\end{align}
with associated measures
\begin{align}
  d\pmb\phi=d\mathbf x\,\frac{d\hat{\mathbf x}}{(2\pi)^N}\,d\bar{\pmb\eta}\,d\pmb\eta
  &&
  d\pmb\sigma=d\pmb\omega\,\frac{d\hat{\pmb\omega}}{(2\pi)^{M+1}}\,d\bar{\pmb\gamma}\,d\pmb\gamma
\end{align}
The Euler characteristic can be expressed using these supervectors as
\begin{align} \label{eq:kac-rice.super}
  &\chi(\Omega)
  =\int d\pmb\phi\,d\pmb\sigma\,e^{\int d1\,L(\pmb\phi(1),\pmb\sigma(1))} \\
  &=\int d\pmb\phi\,d\pmb\sigma\,\exp\left\{
    \int d1\left[
      H\big(\pmb\phi(1)\big)
      +\frac12\sigma_0(1)\left(\|\pmb\phi(1)\|^2-N\right)
      +\sum_{k=1}^M\sigma_k(1)\Big(V_k\big(\pmb\phi(1)\big)-V_0\Big)
    \right]
  \right\} \notag
\end{align}
where $d1=d\bar\theta_1\,d\theta_1$ is the integral over both Grassmann
indices. Since this is an exponential integrand linear in the Gaussian
functions $V_k$, we can take their average to find
\begin{equation} \label{eq:χ.post-average}
  \begin{aligned}
    \overline{\chi(\Omega)}
    =\int d\pmb\phi\,d\pmb\sigma\,\exp\Bigg\{
      \int d1\left[
        H(\pmb\phi(1))
        +\frac12\sigma_0(1)\big(\|\pmb\phi(1)\|^2-N\big)
        -V_0\sum_{k=1}^M\sigma_k(1)
      \right] \\
      +\frac12\int d1\,d2\,\sum_{k=1}^M\sigma_k(1)\sigma_k(2)f\left(\frac{\pmb\phi(1)\cdot\pmb\phi(2)}N\right)
    \Bigg\}
  \end{aligned}
\end{equation}
This is a super-Gaussian integral in the super-Lagrange multipliers $\sigma_k$ with $1\leq k\leq M$.
Performing that integral yields
\begin{equation}
  \begin{aligned}
    \overline{\chi(\Omega)}
    &=\int d\pmb\phi\,d\sigma_0\,\exp\Bigg\{
      \int d1\left[
        H(\pmb\phi(1))
        +\frac12\sigma_0(1)\big(\|\pmb\phi(1)\|^2-N\big)
      \right] \\
    &\hspace{5em}-\frac M2V_0^2\int d1\,d2\,f\left(\frac{\pmb\phi(1)\cdot\pmb\phi(2)}N\right)^{-1}
    -\frac M2\log\operatorname{sdet}f\left(\frac{\pmb\phi(1)\cdot\pmb\phi(2)}N\right)
  \Bigg\}
  \end{aligned}
\end{equation}
The supervector $\pmb\phi$ enters this expression as a function only of the
scalar product with itself and with the vector $\mathbf x_0$ inside the
height function $H(\mathbf x)=\mathbf x_0\cdot\mathbf x$. We therefore make a change
of variables to the superoperator $\mathbb Q$ and the supervector $\mathbb M$
defined by
\begin{equation}
  \mathbb Q(1,2)=\frac{\pmb\phi(1)\cdot\pmb\phi(2)}N
  \qquad
  \mathbb M(1)=\frac{\pmb\phi(1)\cdot\mathbf x_0}N
\end{equation}
These new variables can replace $\pmb\phi$ in the integral using a generalized Hubbard--Stratonovich transformation, which yields
\begin{align}
  &\overline{\chi(\Omega)}
    =\frac12\int d\mathbb Q\,d\mathbb M\,d\sigma_0\,
    \left([\operatorname{sdet}(\mathbb Q-\mathbb M\mathbb M^T)]^\frac12+O(N^{-1})\right)
    \,\exp\Bigg\{
    \frac N2\log\operatorname{sdet}(\mathbb Q-\mathbb M\mathbb M^T)
  \label{eq:post.hubbard-strat}
    \\
  &+N\int d1\left[
        \mathbb M(1)
        +\frac12\sigma_0(1)\big(\mathbb Q(1,1)-1\big)
      \right]
    -\frac M2V_0^2\int d1\,d2\,f(\mathbb Q)^{-1}(1,2)
    -\frac M2\log\operatorname{sdet}f(\mathbb Q)
  \Bigg\}
  \notag
\end{align}
where we show the asymptotic value of the prefactor in Appendix~\ref{sec:prefactor}.
To move on from this expression,
we need to expand the superspace notation. We can write
\begin{equation} \label{eq:ops.q}
  \begin{aligned}
    \mathbb Q(1,2)
    &=C-R(\bar\theta_1\theta_1+\bar\theta_2\theta_2)
    -G(\bar\theta_1\theta_2+\bar\theta_2\theta_1)
    -D\bar\theta_1\theta_1\bar\theta_2\theta_2 \\
    &\qquad
    +(\bar\theta_1+\bar\theta_2)H
    +\bar H(\theta_1+\theta_2)
    -(\bar\theta_1\theta_1\bar\theta_2+\bar\theta_2\theta_2\bar\theta_1)i\hat H
    -\bar{\hat H}(\theta_1\bar\theta_2\theta_2+\theta_1\bar\theta_1\theta_1)
  \end{aligned}
\end{equation}
and
\begin{equation}
  \mathbb M(1)
  =m+\bar\theta_1H_0+\bar H_0\theta_1+i\hat m\bar\theta_1\theta_1
  \label{eq:ops.m}
\end{equation}
with associated measures
\begin{align}
  d\mathbb Q
  =dC\,dR\,dG\,\frac{dD}{(2\pi)^2}\,d\bar H\,dH\,d\bar{\hat H}\,d\hat H
  &&
  d\mathbb M=dm\,\frac{d\hat m}{2\pi}\,d\bar H_0\,dH_0
  \label{eq:op.measures}
\end{align}
The order parameters $C$, $R$, $G$, $D$, $m$, and $\hat m$ are ordinary numbers defined by
\begin{align} \label{eq:ops.bos}
  C=\frac{\mathbf x\cdot\mathbf x}N
  &&
  R=-i\frac{\mathbf x\cdot\hat{\mathbf x}}N
  &&
  G=\frac{\bar{\pmb\eta}\cdot\pmb\eta}N
  &&
  D=\frac{\hat{\mathbf x}\cdot\hat{\mathbf x}}N
  &&
  m=\frac{\mathbf x_0\cdot\mathbf x}N
  &&
  \hat m=-i\frac{\mathbf x_0\cdot\hat{\mathbf x}}N
\end{align}
while $\bar H$, $H$, $\bar{\hat H}$, $\hat H$, $\bar H_0$ and $H_0$ are Grassmann numbers defined by
\begin{align} \label{eq:ops.ferm}
  \bar H=\frac{\bar{\pmb\eta}\cdot\mathbf x}N
  &&
  H=\frac{\pmb\eta\cdot\mathbf x}N
  &&
  \bar{\hat H}=-i\frac{\bar{\pmb\eta}\cdot\hat{\mathbf x}}N
  &&
  \hat H=-i\frac{\pmb\eta\cdot\hat{\mathbf x}}N
  &&
  \bar H_0=\frac{\bar{\pmb\eta}\cdot\mathbf x_0}N
  &&
  H_0=\frac{\pmb\eta\cdot\mathbf x_0}N
\end{align}
We can treat the integral over $\sigma_0$ immediately. It gives
\begin{equation} \label{eq:sigma0.integral}
  \int d\sigma_0\,e^{N\int d1\,\frac12\sigma_0(1)(\mathbb Q(1,1)-1)}
  =2\times2\pi\delta(C-1)\,\delta(G+R)\,\bar HH
\end{equation}
This therefore sets $C=1$ and $G=-R$ in the remainder of the integrand, as well
as removing all dependence on $\bar H$ and $H$. With these solutions inserted, the remaining terms in the exponential give
\begin{align} \label{eq:sdet.q}
  &\operatorname{sdet}(\mathbb Q-\mathbb M\mathbb M^T)
  =1+\frac{(1-m^2)D+\hat m^2-2Rm\hat m}{R^2}
  -\frac6{R^4}\bar H_0H_0\bar{\hat H}\hat H
  \\
  &\hspace{5pc}+\frac2{R^3}\left[
    (mR-\hat m)(\bar{\hat H}H_0+\bar H_0\hat H)
    -(D+R^2)\bar H_0H_0
    +(1-m^2)\bar{\hat H}\hat H
  \right] \notag
  \\ \label{eq:sdet.fq}
  &\operatorname{sdet}f(\mathbb Q)
  =1+\frac{Df(1)}{R^2f'(1)}
  +\frac{2f(1)}{R^3f'(1)}\bar{\hat H}\hat H
  \\ \label{eq:inv.q}
  &\int d1\,d2\,f(\mathbb Q)^{-1}(1,2)
  =\left(f(1)+\frac{R^2f'(1)}{D}\right)^{-1}
  +2\frac{Rf'(1)}{(Df(1)+R^2f'(1))^2}\bar{\hat H}\hat H
\end{align}
The Grassmann terms in these expressions do not contribute to the effective
action, but will be important in our derivation of the prefactor for the
calculation in the connected regime. The substitution of these expressions into \eqref{eq:post.hubbard-strat} without the Grassmann terms yields \eqref{eq:euler.action} from the main text.

\section{Calculation of the prefactor of the average Euler characteristic}
\label{sec:prefactor}


Because of our convention of including the appropriate factors of $2\pi$ in the
superspace measure, super-Gaussian integrals do not produce such factors in our
derivation. Prefactors to our calculation come from three sources: the
introduction of $\delta$-functions to define the order parameters, integrals
over Grassmann order parameters, and from the saddle point approximation to the
large-$N$ integral. In addition, there are important contributions of a sign of the magnetization at the solution that arise from our super-Gaussian integrations.

\subsection{Contribution from the Hubbard--Stratonovich transformations}
\label{sec:prefactor.hs}

First, we examine the factors arising from the definition of order parameters. This begins by introducing to the integral the factor of one
\begin{equation}
  1=(2\pi)^3\int d\mathbb Q\,d\mathbb M\,
  \delta\big(N\mathbb Q(1,2)-\pmb\phi(1)\cdot\pmb\phi(2)\big)\,
  \delta\big(N\mathbb M(1)-\mathbf x_0\cdot\pmb\phi(1)\big)
\end{equation}
where three factors of $2\pi$ come from the measures as defined in
\eqref{eq:op.measures}. Converting the $\delta$-function into an exponential
integral yields
\begin{equation}
  \begin{aligned}
    1=\frac12\int d\mathbb Q\,d\mathbb M\,d\tilde{\mathbb Q}\,d\tilde{\mathbb M}\,
    \exp\Bigg\{
      \frac12\int d1\,d2\,\tilde{\mathbb Q}(1,2)\big(N\mathbb Q(1,2)-\pmb\phi(1)\cdot\pmb\phi(2)\big) \qquad \\
      +\int d1\,i\tilde{\mathbb M}(1)\big(N\mathbb M(1)-\mathbf x_0\cdot\pmb\phi(1)\big)
    \Bigg\}
  \end{aligned}
\end{equation}
where the supervectors and measures for $\tilde{\mathbb Q}$ and $\tilde{\mathbb M}$ are defined
analogously to those of $\mathbb Q$ and $\mathbb M$. This is now a super-Gaussian integral in $\pmb\phi$, which can be performed to yield
\begin{equation}
  \begin{aligned}
  \int d\pmb\phi\,1=\frac12\int d\mathbb Q\,d\mathbb M\,d\tilde{\mathbb Q}\,d\tilde{\mathbb M}\,
  \exp\Bigg\{
    \frac N2\int d1\,d2\,\tilde{\mathbb Q}(1,2)\mathbb Q(1,2)
    +N\int d1\,i\tilde{\mathbb M}(1)\mathbb M(1) \quad \\
    -\frac N2\log\operatorname{sdet}\tilde{\mathbb Q}
    -\frac N2\int d1\,d2\,\tilde{\mathbb M}(1)\tilde{\mathbb Q}^{-1}(1,2)\tilde{\mathbb M}(2)
  \Bigg\}
  \end{aligned}
\end{equation}
We can perform the remaining Gaussian integral in $\tilde{\mathbb M}$ to find
\begin{equation}
  \begin{aligned}
    \int d\pmb\phi\,1=
    \frac12\int d\mathbb Q\,d\mathbb M\,d\tilde{\mathbb Q}\,
    (\operatorname{sdet}\tilde{\mathbb Q}^{-1})^{-\frac12}
    \exp\Bigg\{
      -\frac N2\log\operatorname{sdet}\tilde{\mathbb Q}
      \hspace{5em} \\
      \frac N2\int d1\,d2\,\tilde{\mathbb Q}(1,2)\big[
        \mathbb Q(1,2)-\mathbb M(1)\mathbb M(2)
      \big]
    \Bigg\}
  \end{aligned}
\end{equation}
The integral over $\tilde{\mathbb Q}$ can be evaluated to leading order using the saddle point
method. The integrand is stationary at $\tilde{\mathbb Q}=(\mathbb Q-\mathbb
M\mathbb M^T)^{-1}$, and substituting this into the above expression results in the term
$\frac12\log\det(\mathbb Q-\mathbb M\mathbb M^T)$ in the effective action from
\eqref{eq:post.hubbard-strat}. The saddle point also yields a prefactor of the form
\begin{equation}
  \left(\operatorname{sdet}_{\{1,2\},\{3,4\}}\frac{\partial^2\frac12\log\operatorname{sdet}\tilde{\mathbb Q}}{\partial\tilde{\mathbb Q}(1,2)\partial\tilde{\mathbb Q}(3,4)}\right)^{-\frac12}
  =\left(\operatorname{sdet}_{\{1,2\},\{3,4\}}\tilde{\mathbb Q}^{-1}(3,1)\tilde{\mathbb Q}^{-1}(2,4)\right)^{-\frac12}
  =1
\end{equation}
where the final superdeterminant is identically 1 for any superoperator $\tilde{\mathbb Q}$, not just its saddle-point value.
The Hubbard--Stratonovich transformation therefore contributes a factor of
\begin{equation}
  \frac12\operatorname{sdet}(\mathbb Q-\mathbb M\mathbb M^T)^{\frac12}
  =\frac12[(C-m^2)(D+\hat m^2)+(R-m\hat m)^2]^\frac12G^{-1}
\end{equation}
to the prefactor at the largest order in $N$.

\subsection{Sign of the prefactor}

The superspace notation papers over some analytic differences between branches
of the logarithm that are not important for determining the saddle point but
are important to getting correctly the sign of the prefactor. For instance, consider the superdeterminant of $\mathbb Q$ from \eqref{eq:ops.q} (dropping the fermionic order parameters for a moment for brevity),
\begin{equation}
  \operatorname{sdet}\mathbb Q=\frac{CD+R^2}{G^2}
\end{equation}
The numerator and denominator arise from the determinant in the sector of number and Grassmann number basis elements for the superoperator, respectively. In our calculation, such superdeterminants appear after Gaussian integrals, which produce
\begin{equation}
  \int d\pmb\phi\,\exp\left\{-\frac12\int d1\,d2\,\pmb\phi(1)\mathbb Q(1,2)\pmb\phi(2)\right\}
  =(\operatorname{sdet}\mathbb Q)^{-\frac12}
  =(CD+R^2)^{-\frac12}G
\end{equation}
Here we emphasize that in the expanded result of the integral, the term from
the denominator of the square root enters not as $(G^2)^{\frac12}=|G|$ but as
$G$, including its sign. Therefore, when we write in the effective action
$\frac12\log\operatorname{sdet}\mathbb Q$, we should really be writing
\begin{equation}
  \int d\pmb\phi\,\exp\left\{-\frac12\int d1\,d2\,\pmb\phi(1)\mathbb Q(1,2)\pmb\phi(2)\right\}
  =\operatorname{sign}(G)e^{-\frac12\log\operatorname{sdet}\mathbb Q}
\end{equation}
In our calculation in Appendix \ref{sec:euler} we elide this several times,
and accumulate $M$ factors of $\operatorname{sign}(-Gf'(C))=\operatorname{sign}(-G)$ from the Gaussian
integral over Lagrange multipliers and $N$ factors of $\operatorname{sign}(-G)$
from the Hubbard--Stratonovich transformation. Since at all saddle points $G=-R$, we have
\begin{equation}
  \operatorname{sign}(R)^{N+M}e^{N\mathcal S_\chi(\tilde{\mathbb Q},\mathbb Q,\mathbb M)}
\end{equation}

\subsection{Contribution from integrating the Grassmann order parameters}

After integrating out the Lagrange multiplier enforcing the spherical
constraint in \eqref{eq:sigma0.integral}, the Grassmann variables $\bar H$ and
$H$ are eliminated from the integrand. This leaves dependence on $\bar{\hat
H}$, $\hat H$, $\bar H_0$, and $H_0$. Expanding the contributions from
\eqref{eq:sdet.q}, \eqref{eq:sdet.fq}, and \eqref{eq:inv.q}, the contribution to the action is given by
\begin{equation}
  \int d\bar{\hat H}\,d\hat H\,d\bar H_0\,dH_0\,\exp\left\{
    N\begin{bmatrix}
      \bar{\hat H} & \bar H_0
    \end{bmatrix}
    \begin{bmatrix}
      h_1 & h_2 \\
      h_2 & h_3
    \end{bmatrix}
    \begin{bmatrix}
      \hat H \\
      H_0
    \end{bmatrix}
    +Nh_4\bar{\hat H}\hat H\bar H_0H_0
  \right\}
  =N^2(h_1h_3-h_2^2)+Nh_4
\end{equation}
where
\begin{align}
  &h_1=\frac1R\left(
    \frac{1-m^2}{D(1-m^2)+R^2-2Rm\hat m+\hat m^2}
    -\alpha\frac{Df(1)^2+R^2f'(1)[V_0^2+f(1)]}{[Df(1)+R^2f'(1)]^2}
  \right)
  \\
  &h_2=\frac1R\frac{Rm-\hat m}{D(1-m^2)+R^2-2Rm\hat m+\hat m^2}
  \qquad
  h_3=-\frac1R\frac{D+R^2}{D(1-m^2)+R^2-2Rm\hat m+\hat m^2}
  \\
  &h_4=-\frac1{R^2}\frac1{D(1-m^2)+R^2-2Rm\hat m+\hat m^2}
\end{align}
The contribution at leading order in $N$ is therefore
\begin{equation}
  \frac{N^2}{R^2[D(1-m^2)+R^2-2Rm\hat m+\hat m^2]}\left(
    \alpha\frac{(D+R^2)[Df(1)^2+R^2f'(1)[V_0^2+f(1)]]}{
      [Df(1)+R^2f'(1)]^2
    }
    -1
  \right)
\end{equation}

\subsection{Contribution from the saddle point approximation}

We now want to evaluate the prefactor for the asymptotic value of
$\overline{\chi(\Omega)}$. From the previous sections, the definition of the
measures $d\mathbb Q$ and $d\mathbb M$ in \eqref{eq:op.measures}, and the
integral over $\sigma_0$ of \eqref{eq:sigma0.integral}, we can now see that the
function $g(R,D,m,\hat m)$ of \eqref{eq:pre-saddle.characteristic} is given by
\begin{equation}
  \begin{aligned}
    g(R,D,m,\hat m)
    =-
    \frac{\operatorname{sign}(R)^{N+M}}{R^3[D(1-m^2)+R^2-2Rm\hat m+\hat m^2]^{\frac12}}
    \hspace{3pc} \\
    \times\left(
      \alpha\frac{(D+R^2)[Df(1)^2+R^2f'(1)[V_0^2+f(1)]]}{
        [Df(1)+R^2f'(1)]^2
      }
      -1
    \right)
  \end{aligned}
\end{equation}
In the connected regime, there are two saddle points of the integrand that
contribute to the asymptotic value of the integral, at $m=\pm m^*$ with $R=-m^*$, $D=0$, and $\hat m=0$. At this saddle point $\mathcal S_\chi=0$. We can therefore write
\begin{equation}
  \overline{\chi(\Omega)}
  =\sum_{m=\pm m^*}
    g(-m,0,m,0)\big[\det\partial\partial\mathcal S_\chi(-m,0,m,0)\big]^{-\frac12}
\end{equation}
where here $\partial=[\frac{\partial}{\partial R},\frac{\partial}{\partial D},\frac\partial{\partial m},\frac\partial{\partial \hat m}]$ is the
vector of derivatives with respect to the remaining order parameters. For both of the two saddle points, the determinant of the Hessian of the effective action evaluates to
\begin{equation}
  \det\partial\partial\mathcal S_\chi
  =\left[\frac1{(m^*)^4}\left(1-\frac{\alpha[V_0^2+f(1)]}{f'(1)}\right)\right]^2
\end{equation}
whereas
\begin{equation}
  g(\mp m^*,0,\pm m^*,0)
  =\frac{(\mp1)^{N+M+1}}{|m^*|^4}\left(1-\frac{\alpha[V_0^2+f(1)]}{f'(1)}\right)
\end{equation}
The saddle point at $m=-m^*$, characterized by minima of the height function, always contributes with a positive term. On the other hand, the saddle point with $m=+m^*$, characterized by maxima of the height function, contributes with a sign depending on if $N+M+1$ is even or odd. This follows from the fact that minima, with an index of 0, have a positive contribution to the sum over stationary points, while maxima, with an index of $N-M-1$, have a contribution that depends on the dimension of the manifold.

We have finally that, in the connected regime,
\begin{equation}
  \overline{\chi(\Omega)}=1+(-1)^{N+M+1}+O(N^{-1})
\end{equation}
When $N+M+1$ is odd, this evaluates to zero. In fact it must be zero to all
orders in $N$, since for odd-dimensional manifolds the Euler characteristic is
always zero. When $N+M+1$ is even, we have $\overline{\chi(\Omega)}=2$ to
leading order in $N$, as specified in the main text.

\section{The average squared Euler characteristic}
\label{sec:rms}

\subsection{Derivation}

Here we calculate $\overline{\chi(\Omega)^2}$, the average of the squared Euler
characteristic. This is accomplished by taking two copies of the integral \eqref{eq:kac-rice.super}, with
\begin{equation}
  \chi(\Omega)^2
  =\int d\pmb\phi_1\,d\pmb\sigma_1\,d\pmb\phi_2\,d\pmb\sigma_2\,
  e^{\int d1\,[L(\pmb\phi_1(1),\pmb\sigma_1(1))+L(\pmb\phi_2(1),\pmb\sigma_2(1))]}
\end{equation}
The same steps as in the derivation of the Euler characteristic follow. The result is the same as \eqref{eq:post.hubbard-strat}, but with the substitutions
\begin{align}
  \mathbb Q\mapsto\begin{bmatrix}
    \mathbb Q_{11} & \mathbb Q_{12} \\
    \mathbb Q_{21} & \mathbb Q_{22}
  \end{bmatrix}
  &&
  \mathbb M\mapsto\begin{bmatrix}
    \mathbb M_1 \\
    \mathbb M_2
  \end{bmatrix}
\end{align}
where we have defined
\begin{align}
  \mathbb Q_{ij}(1,2)=\frac1N\pmb\phi_i(1)\cdot\pmb\phi_j(2)
  &&
  \mathbb M_i(1)=\frac1N\pmb\phi_i(1)\cdot\mathbf x_0
\end{align}
Expanding the superindices and applying the Dirac $\delta$-functions implied by
the Lagrange multipliers associated with the spherical constraint, we arrive at an expression
\begin{equation}
  \overline{\chi(\Omega)^2}
  \simeq\int dC_{12}\,dR_{11}\,dR_{12}\,dR_{21}\,dR_{22}\,dD_{11}\,dD_{12}\,dD_{22}\,dG_{12}\,dG_{21}\,dm_1\,dm_2\,d\hat m_1\,d\hat m_2\,e^{N\mathcal S_{\chi^2}}
\end{equation}
where we have defined another effective action by
\begin{equation}
  \mathcal S_{\chi^2}=-\hat m_1-\hat m_2
  -\frac\alpha2\log\frac{\det A_1}{\det A_2}
  -\frac{\alpha V_0^2}2\begin{bmatrix}
    0 & 1 & 0 & 1
  \end{bmatrix}
  A_1^{-1}
  \begin{bmatrix}
    0 \\ 1 \\ 0 \\ 1
  \end{bmatrix}
  +\frac12\log\frac{\det A_3}{\det A_4}
\end{equation}
with the matrices $A_1$, $A_2$, $A_3$, and $A_4$ defined by
\begin{equation}
  A_1=\begin{bmatrix}
    D_{11}f'(1) & iR_{11}f'(1) & D_{12}f'(C_{12})+\Delta_{12}f''(C_{12}) & i R_{21}f'(C_{12}) \\
    i R_{11}f'(1) & f(1) & i R_{12}f'(C_{12}) & f(C_{12}) \\
    D_{12}f'(C_{12}) + \Delta_{12}f''(C_{12}) & iR_{12}f'(C_{12}) & D_{22} & iR_{22}f'(1) \\
    iR_{21}f'(C_{12}) & f(C_{12}) & iR_{22}f'(1) & f(1)
  \end{bmatrix}
\end{equation}
\begin{equation}
  A_2=\begin{bmatrix}
    0 & R_{11}f'(1) & 0 & -G_{21}f'(C_{12}) \\
    -R_{11}f'(1) & 0 & G_{12}f'(C_{12}) & 0 \\
    0 & -G_{12}f'(C_{12}) & 0 & R_{22}f'(1) \\
    G_{21}f'(C_{12}) & 0 & -R_{22}f'(1) & 0
  \end{bmatrix}
\end{equation}
\begin{equation}
  A_3=\begin{bmatrix}
    1-m_1^2 & i(R_{11}-m_1\hat m_1) & C_{12}-m_1m_2 & i(R_{21}-m_1\hat m_2) \\
    i(R_{11}-m_1\hat m_1) & D_{11}+\hat m_1^2 & i(R_{12}-m_2\hat m_1) & D_{12}+\hat m_1\hat m_2 \\
    C_{12}-m_1m_2 & i(R_{12}-m_2\hat m_1) & 1-m_2^2 & i(R_{22}-m_2\hat m_2) \\
    i(R_{21}-m_1\hat m_2) & D_{12}+\hat m_1\hat m_2 & i(R_{22}-m_2\hat m_2) & D_{22}+\hat m_2^2
  \end{bmatrix}
\end{equation}
\begin{equation}
  A_4=\begin{bmatrix}
    0 & R_{11} & 0 & -G_{21} \\
    -R_{11} & 0 & G_{12} & 0 \\
    0 & -G_{12} & 0 & R_{22} \\
    G_{21} & 0 & -R_{22} & 0
  \end{bmatrix}
\end{equation}
and where $\Delta_{12}=G_{12}G_{21}-R_{12}R_{21}$. The expression must be
extremized over all the order parameters. We look for solutions in two regimes
that are commensurate with the solutions found for the Euler characteristic.
These correspond to $m_1=m_2=0$ and $C_{12}=0$, and $m_1=m_2=\pm m_*$ and
$C_{12}=1$. We restrict ourselves to cases with $f(0)=0$, which correspond to constraint equations without a random constant term. We find such solutions, and in all cases they have
\begin{align}
  G_{12}=G_{21}=R_{12}=R_{21}=D_{12}=\hat m_1=\hat m_2=0
  \\
  D_{ii}=-\frac{m+R_{ii}}{1-m^2}R_{ii}
  \hspace{2em}
  R_{22}=R_{11}^\dagger
  \hspace{2em}
  R_{11}=R_*
\end{align}
where $\dagger$ denotes the complex conjugate and $R_*$ is the saddle point
solution of \eqref{eq:rs}. Upon substituting these solutions into the
expressions above, we find in both cases that
\begin{equation}
  \mathcal S_{\chi^2}=2\operatorname{Re}\mathcal S_\chi
\end{equation}
as referenced in the main text. This corresponds with
$\overline{\chi(\Omega)^2}\simeq[\overline{\chi(\Omega)}]^2$, justifying the
`annealed' approach we have taken here.

\subsection{Instability to replica symmetry breaking}

However, these solutions are not always the correct saddle point for evaluating
the average squared Euler characteristic. When another solution is dominant,
the dissonance between the average square and squared average indicates the
necessity of a quenched calculation to determine the behavior of typical
samples, and likely instability to \textsc{rsb}. We can find these points of
instability by examining the Hessian of the action of the average square of the
Euler characteristic at $m=0$. The stability of this matrix is not sufficient
to determine if our solution is stable, since the many $\delta$-functions
employed in our derivation ensure that the resulting saddle point never at a
true maximum with respect to some combinations of variables. We rather look for
places where the stability of this matrix changes, which represent places where
another solution branches from the existing one. However, we must neglect the
branching of trivial solutions, which occur when $R_*$ goes from real- to
complex-valued.

By examination of the results, it appears that nontrivial \textsc{rsb}
instabilities occur along eigenvectors of the Hessian of $\mathcal S_{\chi^2}$
in the subspace spanned by $C_{12}$, $R_{12}$, $R_{21}$, and $D_{12}$. This may
not be surprising, since these are the parameters that represent nontrivial
correlations between the two copies of the system. We can therefore find the \textsc{rsb} instability by looking for nontrivial zeros of
\begin{equation}
  \det\partial\partial\mathcal S_{\chi^2}\equiv\det\frac{\partial^2\mathcal S_{\chi^2}}{\partial[C_{12},R_{12},R_{21},D_{12}]^2}
\end{equation}
evaluated at the $m=0$ solution described above. The resulting expression is
usually quite heinous, but there is a regime where a dramatic simplification is
possible. The instability always occurs along the direction
$R_{21}=R_{12}^\dagger$, but when $R_*$ is real, $R_{11}=R_{22}$ and the instability occurs along
the direction $R_{21}=R_{12}$. This allows us to examine a simpler action, and
we find the determinant is proportional to two nontrivial factors, with
\begin{equation} \label{eq:stab.det}
  \det\partial\partial\mathcal S_{\chi^2}=-\frac{2B_1B_2}{[r_*f'(1)]^3[(1+r_*)f(1)-r_*f'(1)]^7}
\end{equation}
If we let
$r_*=\lim_{m\to0}R_*/m$, then the factors $B_1$ and $B_2$ are
\begin{align}
  B_1=[(1+r_*)f(1)]^3-3r_*[(1+r_*)f(1)]^2f'(1)
  +\alpha V_0^2\big[2(1+r_*)f'(0)^2+r_*f'(1)f''(0)\big]
  \quad
  \notag \\
  +\alpha r_*f'(0)^2f'(1)-[r_*f'(1)]^3
  -(1+r_*)f(1)\Big(\alpha\big[f'(0)^2+V_0^2f''(0)\big]-3[r_*f'(1)]^2\Big)
\end{align}
\begin{align}
  &B_2=\big[(1+r_*)f(1)-r_*f'(1)\big]^3[f'(1)^2-\alpha f'(0)^2]
  \Big(f'(1)\big[(1+r_*)f(1)-r_*f'(1)\big]-\alpha f'(0)^2\Big)
  \notag \\
  &\qquad-[\alpha V_0^2r_*f'(1)]^2f''(0)\big[(1+r_*)f'(0)^2+r_*f'(1)f''(0)\big]
  \notag \\
  &\qquad-\alpha V_0^2\big[(1+r_*)f(1)-r_*f'(1)\big]^2
  \bigg[
    (1+r_*)f'(0)^2\big[\alpha f'(0)^2-f'(1)^2\big]
    \notag \\
  &\qquad\qquad+
    r_*f'(1)f''(0)\bigg(
      \alpha f'(0)^2\frac{(1+r_*)f(1)-2r_*f'(1)}{(1+r_*)f(1)-r_*f'(1)}
      -(1-r_*)f'(1)^2
    \bigg)
  \bigg]
\end{align}
As $\alpha$ is increased from zero, the first of these factors to go through
zero represents the instability point. These formulas are responsible for the
shaded regions in Fig.~\ref{fig:phases} and Fig.~\ref{fig:crossover}.

Surprisingly, this approach sees no signal of a
replica symmetry breaking (\textsc{rsb}) transition previously found in
\cite{Urbani_2023_A}. The
instability is predicted to occur when
\begin{equation} \label{eq:vrsb}
  V_0^2>V_\text{\textsc{rsb}}^2
  \equiv\frac{[f(1)-f(0)]^2}{\alpha f''(0)}
  -f(0)-\frac{f'(0)}{f''(0)}
\end{equation}
We conjecture that the \textsc{rsb}
instability found in \cite{Urbani_2023_A} is a trait of the cost function
\eqref{eq:cost}, and is not inherent to the structure of the solution manifold.
Perhaps the best evidence for this is to consider the limit of $M=1$, or
$\alpha\to0$ with $E=V_0\sqrt\alpha$ held fixed, where this problem reduces to
the level sets of the spherical spin glasses. The instability \eqref{eq:vrsb}
implies for the pure spherical 2-spin model with $f(q)=\frac12q^2$ that
$E_\textsc{rsb}=\frac12$, though nothing of note is known to occur in the level
sets of 2-spin model at such an energy.

\section{The quenched shattering energy}
\label{sec:1frsb}

Here we share how the quenched shattering energy is calculated under a
{\oldstylenums1}\textsc{frsb} ansatz. To best make contact with prior work on
the spherical spin glasses, we start with \eqref{eq:χ.post-average}. The
formula in a quenched calculation is almost the same as that for the annealed,
but the order parameters $C$, $R$, $D$, and $G$ must be understood as $n\times
n$ matrices rather than scalars. In principle $m$, $\hat m$, $\omega_0$, $\hat\omega_0$, $\omega_1$, and $\hat\omega_1$ should be considered $n$-dimensional vectors, but since in our ansatz replica vectors are constant we can take them to be constant from the start. We have
\begin{align}
  &\overline{\log\chi(\Omega)}
  =\lim_{n\to0}\frac\partial{\partial n}\int dC\,dR\,dD\,dG\,dm\,d\hat m\,d\omega_0\,d\hat\omega_0\,d\omega_1\,d\hat\omega_1\,
  \exp N\Bigg\{
    n\hat m
    +\frac i2\hat\omega_0\operatorname{Tr}(C-I)
    \notag \\
  &\hspace{2em}-\omega_0\operatorname{Tr}(G+R)
    -in\hat\omega_1E_0
    +\frac12\log\det\begin{bmatrix}
      C-m^2 & i(R-m\hat m) \\ i(R-m\hat m) & D-\hat m^2
    \end{bmatrix}
    -\frac12\log G^2
    \notag \\
    &\hspace{2em}-\frac12\sum_{ab}^n\left[
      \hat\omega_1^2f(C_{ab})
      +(2i\omega_1\hat\omega_1R_{ab}+\omega_1^2D_{ab})f'(C_{ab})
      +\omega_1^2(G_{ab}^2-R_{ab}^2)f''(C_{ab})
    \right]
  \Bigg\}
\end{align}
which is completely general for the spherical spin glasses with $M=1$. We now
make a series of simplifications. Ward identities associated with the BRST
symmetry possessed by the original action \cite{Annibale_2003_The, Annibale_2003_Supersymmetric, Annibale_2004_Coexistence} indicate that
\begin{align}
  \omega_1D=-i\hat\omega_1R
  &&
  G=-R
  &&
  \hat m=0
\end{align}
Moreover, this problem with $m=0$ has a close resemblance to the complexity of
the spherical spin glasses. In both, at the supersymmetric saddle point the
matrix $R$ is diagonal with $R=r_dI$ \cite{Kent-Dobias_2023_How}.
To investigate the shattering energy, we can restrict to solutions with $m=0$
and look for the place where such solutions vanish. Inserting these simplifications, we have up to highest order in $N$
\begin{equation}
  \begin{aligned}
    \overline{\log\chi(\Omega)}
    =\lim_{n\to0}\frac\partial{\partial n}\int dC\,dR\,d\hat\omega_0\,d\omega_1\,d\hat\omega_1\,
    \exp N\Bigg\{
      \frac i2\hat\omega_0\operatorname{Tr}(C-I)
      -in\hat\omega_1E
      \qquad\\
      -i\frac12n\omega_1\hat\omega_1r_df'(1)
      -\frac12\sum_{ab}^n
        \hat\omega_1^2f(C_{ab})
      +\frac12\log\det
      \left(\frac{-i\hat\omega_1}{\omega_1r_d}C+I\right)
    \Bigg\}
  \end{aligned}
\end{equation}
If we redefine $\hat\beta=-i\hat\omega_1$ and $\tilde r_d=\omega_1 r_d$, we find
\begin{equation}
  \begin{aligned}
    \overline{\log\chi(\Omega)}
    =\lim_{n\to0}\frac\partial{\partial n}\int dC\,d\hat\beta\,d\tilde r_d\,\hat\omega_0\,
    \exp N\Bigg\{
      \frac i2\hat\omega_0\operatorname{Tr}(C-I)
      +n\hat\beta E
      \qquad\\
      +n\frac12\hat\beta\tilde r_df'(1)
      +\frac12\sum_{ab}^n
        \hat\beta^2f(C_{ab})
      +\frac12\log\det
      \left(\frac{\hat\beta}{\tilde r_d}C+I\right)
    \Bigg\}
  \end{aligned}
\end{equation}
which is exactly the effective action for the supersymmetric complexity in the
spherical spin glasses when in the regime where minima dominate
\cite{Kent-Dobias_2023_How}. As the effective action for the Euler characteristic, this expression is valid whether minima dominate or not. Following the same steps as in
\cite{Kent-Dobias_2023_How}, we can write the continuum version of this action
for arbitrary \textsc{rsb} structure in the matrix $C$ as
\begin{equation} \label{eq:cont.action}
  \frac1N\overline{\log\chi(\Omega)}=\hat\beta E+\frac12\hat\beta\tilde r_df'(1)
  +\frac12\int_0^1dq\,\left[
    \hat\beta^2f''(q)\chi(q)+\frac1{\chi(q)+\tilde r_d\hat\beta^{-1}}
  \right]
\end{equation}
where $\chi(q)=\int_1^qdq'\int_0^{q'}dq''P(q'')$ and $P(q)$ is the distribution of
off-diagonal elements of the matrix $C$ \cite{Crisanti_1992_The, Crisanti_2004_Spherical, Crisanti_2006_Spherical}. This action must be extremized over
the function $\chi$ and the variables $\hat\beta$ and $\tilde r_d$, under the
constraint that $\chi(q)$ is continuous, that it has $\chi'(1)=-1$, and
$\chi(1)=0$, necessary for $P$ to be a well-defined probability distribution.

Now the specific form of replica symmetry breaking we expect to see is
important. We want to study the mixed $2+s$ models in the regime where they may
have 1-full \textsc{rsb} in equilibrium \cite{Auffinger_2022_The}. For the Euler characteristic like the
complexity, this will correspond to full \textsc{rsb}, in an analogous way to
{\oldstylenums1}\textsc{rsb} equilibria give a \textsc{rs} complexity. Such order is characterized by a piecewise smooth $\chi$ of the form
\begin{equation}
  \chi(q)=\begin{cases}
    \chi_0(q) & q < q_0 \\
    1-q & q \geq q_0
  \end{cases}
\end{equation}
where $\chi_0$ is
\begin{equation}
  \chi_0(q)=\frac1{\hat\beta}[f''(q)^{-1/2}-\tilde r_d]
\end{equation}
the function implied by extremizing \eqref{eq:cont.action} over $\chi$. The
variable $q_0$ must be chosen so that $\chi$ is continuous. The key difference
between \textsc{frsb} and {\oldstylenums1}\textsc{frsb} in this setting is that
in the former case the ground state has $q_0=1$, while in the latter the ground
state has $q_0<1$.

We use this action to find the shattering energy in the following way. First,
we know that the ground state energy is the place where the manifold and therefore the average Euler characteristic vanishes. Therefore, setting $\overline{\log\chi(\Omega)}=0$ and solving for $E$ yields a formula
for the ground state energy
\begin{equation}
  E_\text{gs}=-\frac1{\hat\beta}\left\{
    \frac12\hat\beta\tilde r_df'(1)
    +\frac12\int_0^1dq\,\left[
      \hat\beta^2f''(q)\chi(q)+\frac1{\chi(q)+\tilde r_d\hat\beta^{-1}}
    \right]
  \right\}
\end{equation}
This expression can be maximized over $\hat\beta$ and $\tilde r_d$ to find the
correct parameters at the ground state for a particular model. Then, the
shattering energy is found by slowly lowering $q_0$ and solving the combined
extremal and continuity problem for $\hat\beta$, $\tilde r_d$, and $E$ until
$E$ reaches a maximum value and starts to decrease. This maximum is the
shattering energy, since it is the point where the $m=0$ solution vanishes.
Starting from this point, we take small steps in $s$ and $\lambda_s$, again
simultaneously extremizing, ensuring continuity, and maximizing $E$. This draws
out the shattering energy across the entire range of $s$ plotted in
Fig.~\ref{fig:ssg}. The transition to the \textsc{rs} solution occurs when the
value $q_0$ that maximizes $E$ hits zero. We find that the transition between
\textsc{rs} and \textsc{frsb} is precisely predicted by the \textsc{rsb} instability
calculated in Appendix~\ref{sec:rms} by analyzing the solution to the average
square of the Euler characteristic, as shown in Fig.~\ref{fig:rsb}.

\begin{figure}
  \centering
  \includegraphics{figs/rsb_comp.pdf}

  \caption{
    \textbf{Self-consistency between \textsc{rsb} instabilities.}
    Comparison between the predicted value $q_0$ for the \textsc{frsb} solution
    at the shattering energy in $2+s$ models and the value of the determinant
    \eqref{eq:stab.det} used in the previous appendix to predict the point of
    \textsc{rsb} instability. The value of $s$ at which $q_0$ becomes nonzero is
    precisely the point where the determinant has a nontrivial zero.
  } \label{fig:rsb}
\end{figure}

\bibliography{topology}

\end{document}